{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "245418c1",
   "metadata": {},
   "source": [
    "## GReaT Example with Iris Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ab1f4da7-4b93-4055-8d56-6030c7498999",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Execute only once!\n",
    "import os\n",
    "import sys\n",
    "sys.path.append(\"..\")\n",
    "os.chdir(\"..\")\n",
    "\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b5009b62",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import logging\n",
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "71836c07-2530-44af-9b4b-cf72aff34426",
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils import set_logging_level\n",
    "from be_great import GReaT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "00695066",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "39088828",
   "metadata": {},
   "outputs": [],
   "source": [
    "logger = set_logging_level(logging.INFO)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "937c8223",
   "metadata": {},
   "source": [
    "### Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9b77f31e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   target  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = datasets.load_iris(as_frame=True).frame\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5d99184c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.columns = [\"sepal length\", \"sepal width\", \"petal length\", \"petal width\", \"target\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5911b9b1",
   "metadata": {},
   "source": [
    "### Create GReaT Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1a316696",
   "metadata": {},
   "outputs": [],
   "source": [
    "great = GReaT(\"distilgpt2\",                  # Name of the large language model used (see HuggingFace for more options)\n",
    "              epochs=1000,                   # Number of epochs to train\n",
    "              save_steps=2000,               # Save model weights every x steps\n",
    "              logging_steps=500,             # Log the loss and learning rate every x steps\n",
    "              experiment_dir=\"trainer_iris\", # Name of the directory where all intermediate steps are saved\n",
    "              batch_size=16,                 # Batch Size\n",
    "              #lr_scheduler_type=\"constant\", # Specify the learning rate scheduler \n",
    "              #learning_rate=5e-5            # Set the inital learning rate\n",
    "             )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c7b173f",
   "metadata": {},
   "source": [
    "### Start Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "200e000c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[39;20m2022-10-13 09:45:13,702 - INFO - Convert data into HuggingFace dataset object... (great.py:99)\u001b[0m\n",
      "\u001b[39;20m2022-10-13 09:45:13,705 - INFO - Create GReaT Trainer... (great.py:104)\u001b[0m\n",
      "\u001b[39;20m2022-10-13 09:45:15,746 - INFO - Start training... (great.py:113)\u001b[0m\n",
      "/home/kathrin/miniconda3/envs/transformers/lib/python3.9/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  warnings.warn(\n",
      "***** Running training *****\n",
      "  Num examples = 150\n",
      "  Num Epochs = 1000\n",
      "  Instantaneous batch size per device = 16\n",
      "  Total train batch size (w. parallel, distributed & accumulation) = 16\n",
      "  Gradient Accumulation steps = 1\n",
      "  Total optimization steps = 10000\n",
      "You're using a GPT2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
     ]
    },
    {
     "data": {
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       "    <div>\n",
       "      \n",
       "      <progress value='10000' max='10000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [10000/10000 08:37, Epoch 1000/1000]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>0.568400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1000</td>\n",
       "      <td>0.444800</td>\n",
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       "    <tr>\n",
       "      <td>1500</td>\n",
       "      <td>0.414200</td>\n",
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       "    <tr>\n",
       "      <td>2000</td>\n",
       "      <td>0.385300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2500</td>\n",
       "      <td>0.356700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3000</td>\n",
       "      <td>0.338400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3500</td>\n",
       "      <td>0.324600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4000</td>\n",
       "      <td>0.314500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4500</td>\n",
       "      <td>0.308200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5000</td>\n",
       "      <td>0.303100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5500</td>\n",
       "      <td>0.299600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6000</td>\n",
       "      <td>0.295600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6500</td>\n",
       "      <td>0.292700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7000</td>\n",
       "      <td>0.290500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7500</td>\n",
       "      <td>0.288500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8000</td>\n",
       "      <td>0.287100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8500</td>\n",
       "      <td>0.286000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9000</td>\n",
       "      <td>0.284100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9500</td>\n",
       "      <td>0.283800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10000</td>\n",
       "      <td>0.283800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving model checkpoint to trainer_iris/checkpoint-2000\n",
      "Configuration saved in trainer_iris/checkpoint-2000/config.json\n",
      "Model weights saved in trainer_iris/checkpoint-2000/pytorch_model.bin\n",
      "tokenizer config file saved in trainer_iris/checkpoint-2000/tokenizer_config.json\n",
      "Special tokens file saved in trainer_iris/checkpoint-2000/special_tokens_map.json\n",
      "Saving model checkpoint to trainer_iris/checkpoint-4000\n",
      "Configuration saved in trainer_iris/checkpoint-4000/config.json\n",
      "Model weights saved in trainer_iris/checkpoint-4000/pytorch_model.bin\n",
      "tokenizer config file saved in trainer_iris/checkpoint-4000/tokenizer_config.json\n",
      "Special tokens file saved in trainer_iris/checkpoint-4000/special_tokens_map.json\n",
      "Saving model checkpoint to trainer_iris/checkpoint-6000\n",
      "Configuration saved in trainer_iris/checkpoint-6000/config.json\n",
      "Model weights saved in trainer_iris/checkpoint-6000/pytorch_model.bin\n",
      "tokenizer config file saved in trainer_iris/checkpoint-6000/tokenizer_config.json\n",
      "Special tokens file saved in trainer_iris/checkpoint-6000/special_tokens_map.json\n",
      "Saving model checkpoint to trainer_iris/checkpoint-8000\n",
      "Configuration saved in trainer_iris/checkpoint-8000/config.json\n",
      "Model weights saved in trainer_iris/checkpoint-8000/pytorch_model.bin\n",
      "tokenizer config file saved in trainer_iris/checkpoint-8000/tokenizer_config.json\n",
      "Special tokens file saved in trainer_iris/checkpoint-8000/special_tokens_map.json\n",
      "Saving model checkpoint to trainer_iris/checkpoint-10000\n",
      "Configuration saved in trainer_iris/checkpoint-10000/config.json\n",
      "Model weights saved in trainer_iris/checkpoint-10000/pytorch_model.bin\n",
      "tokenizer config file saved in trainer_iris/checkpoint-10000/tokenizer_config.json\n",
      "Special tokens file saved in trainer_iris/checkpoint-10000/special_tokens_map.json\n",
      "\n",
      "\n",
      "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "trainer = great.fit(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cfe8184d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'train_runtime': 517.6539,\n",
       " 'train_samples_per_second': 289.769,\n",
       " 'train_steps_per_second': 19.318,\n",
       " 'total_flos': 1339656192000000.0,\n",
       " 'train_loss': 0.33248889617919924,\n",
       " 'epoch': 1000.0,\n",
       " 'step': 10000}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss_hist = trainer.state.log_history.copy()\n",
    "loss_hist.pop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "76deeb9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "loss = [x[\"loss\"] for x in loss_hist]\n",
    "epochs = [x[\"epoch\"] for x in loss_hist]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bcd7fc6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f04889ec9a0>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(epochs, loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0073ca9",
   "metadata": {},
   "source": [
    "### Save Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4ed119bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "great.save(\"iris\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6076d7aa",
   "metadata": {},
   "source": [
    "### Load Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "62d71bf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# great = GReaT.load_from_dir(\"iris\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdb05fe0",
   "metadata": {},
   "source": [
    "### Generate Samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4bb298ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_samples = 150"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2106b9f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████| 150/150 [00:01<00:00, 99.81it/s]\n"
     ]
    }
   ],
   "source": [
    "samples = great.sample(n_samples, k=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "105aa225",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length</th>\n",
       "      <th>sepal width</th>\n",
       "      <th>petal length</th>\n",
       "      <th>petal width</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length  sepal width  petal length  petal width  target\n",
       "0           5.6          2.8           4.0          1.1     1.0\n",
       "1           7.3          2.9           6.3          1.8     2.0\n",
       "2           5.8          2.7           3.9          1.2     1.0\n",
       "3           5.7          2.5           5.0          2.0     2.0\n",
       "4           6.7          3.0           5.0          1.7     1.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c6f534d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "samples.to_csv(\"iris_samples.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3a734ac",
   "metadata": {},
   "source": [
    "We can also influence the distribution of the column on which the generation is conditioned (In the original dataset all three target values (0, 1, 2) have a probability of 33%)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d6ada426",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████| 150/150 [00:01<00:00, 98.16it/s]\n"
     ]
    }
   ],
   "source": [
    "samples_custom = great.sample(n_samples, k=50, start_col=\"target\",\n",
    "                              start_col_dist={\"0\": 0.5, \"1\": 0.5, \"2\": 0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3510fa2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length</th>\n",
       "      <th>sepal width</th>\n",
       "      <th>petal length</th>\n",
       "      <th>petal width</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length  sepal width  petal length  petal width  target\n",
       "0           5.2          3.4           1.5          0.2     0.0\n",
       "1           4.8          3.0           1.4          0.2     0.0\n",
       "2           6.6          3.0           4.4          1.4     1.0\n",
       "3           6.4          2.9           4.3          1.3     1.0\n",
       "4           5.8          4.0           1.2          0.2     0.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples_custom.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70545fac",
   "metadata": {},
   "source": [
    "## Plot Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3e051cc",
   "metadata": {},
   "source": [
    "Original Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e70c4dfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f049060bc10>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n",
    "\n",
    "ax[0].scatter(data[\"sepal length\"], data[\"sepal width\"], c=data[\"target\"])\n",
    "ax[1].scatter(data[\"petal length\"], data[\"petal width\"], c=data[\"target\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f56b0e03",
   "metadata": {},
   "source": [
    "Generated samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "66d7efcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "samples = pd.read_csv(\"iris_samples.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "96d782e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f04890db3a0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n",
    "\n",
    "ax[0].scatter(samples[\"sepal length\"], samples[\"sepal width\"], c=samples[\"target\"].astype(\"int\"))\n",
    "ax[0].set_ylim(1.8, 4.5)\n",
    "ax[1].scatter(samples[\"petal length\"], samples[\"petal width\"], c=samples[\"target\"].astype(\"int\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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